U.S. patent number 7,693,343 [Application Number 10/580,675] was granted by the patent office on 2010-04-06 for motion-compensated inverse filtering with band-pass filters for motion blur reduction.
This patent grant is currently assigned to Koninklijke Philips Electronics N.V.. Invention is credited to Michiel Adriaanszoon Klompenhouwer, Leo Jan Velthoven.
United States Patent |
7,693,343 |
Klompenhouwer , et
al. |
April 6, 2010 |
Motion-compensated inverse filtering with band-pass filters for
motion blur reduction
Abstract
This invention relates to a method, a computer program, a
computer program product, and a device for reducing motion blur of
images of a video signal shown on a hold-type display (101),
comprising estimating (1102) motion vectors of moving components in
said images of said video signal; band-pass filtering (1100, 1101)
said video signal with respect to a spatial frequency domain,
wherein said band-pass filtering at least partially depends on said
estimated motion vectors, and wherein with increasing length of
said estimated motion vectors, the passband of said band-pass
filtering adaptively shifts from high spatial frequencies to medium
spatial frequencies; and combining (1104) said video signal and
said band-pass filtered video signal to produce an input video
signal for said hold-type display.
Inventors: |
Klompenhouwer; Michiel
Adriaanszoon (Eindhoven, NL), Velthoven; Leo Jan
(Eindhoven, NL) |
Assignee: |
Koninklijke Philips Electronics
N.V. (Eindhoven, NL)
|
Family
ID: |
34639294 |
Appl.
No.: |
10/580,675 |
Filed: |
November 25, 2004 |
PCT
Filed: |
November 25, 2004 |
PCT No.: |
PCT/IB2004/052553 |
371(c)(1),(2),(4) Date: |
May 26, 2006 |
PCT
Pub. No.: |
WO2005/055587 |
PCT
Pub. Date: |
June 16, 2005 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20070126928 A1 |
Jun 7, 2007 |
|
Foreign Application Priority Data
|
|
|
|
|
Dec 1, 2003 [EP] |
|
|
03078779 |
|
Current U.S.
Class: |
382/260;
345/102 |
Current CPC
Class: |
H04N
5/21 (20130101) |
Current International
Class: |
G06K
9/40 (20060101); G09G 3/36 (20060101) |
Field of
Search: |
;382/265,260
;345/102 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Mehta; Bhavesh M
Assistant Examiner: Akhavannik; Hadi
Claims
The invention claimed is:
1. A method for reducing motion blur of images of a video signal
shown on a hold-type display, comprising: estimating motion vectors
of moving components in said images of said video signal; band-pass
filtering said video signal with respect to a spatial frequency
domain, wherein said band-pass filtering at least partially depends
on said estimated motion vectors, and wherein with increasing
length of said estimated motion vectors, the passband of said
band-pass filtering adaptively shifts from high spatial frequencies
to medium spatial frequencies, wherein band-pass filtering includes
anti-blur filtering performed with an anti-blur filter that
comprises a one-dimensional filter with a variable tap spacing that
depends on said length of said estimated motion vectors; and
combining said video signal and said band-pass filtered video
signal to produce an input video signal for said hold-type
display.
2. The method according to claim 1, wherein said band-pass
filtering comprises low-pass filtering and anti-blur filtering in
cascaded form.
3. The method according to claim 2, wherein said anti-blur
filtering is performed with an anti-blur filter that approximates
an inverted low-pass filter.
4. The method according to claim 2, wherein said anti-blur filter
is a one-dimensional filter with fixed filter coefficients and a
variable tap spacing that depends on said length of said estimated
motion vectors.
5. The method according to claim 2, wherein said anti-blur
filtering is performed in the direction of said estimated motion
vectors.
6. The method according to claim 2, wherein said low-pass filtering
is performed in the direction of said estimated motion vectors.
7. The method according to claim 2, wherein said low-pass filtering
is performed both in a direction perpendicular and in a direction
parallel to a direction of said estimated motion vectors.
8. The method according to claim 2, wherein said low-pass filtering
is at least partially implemented by an interpolation of samples of
said images of said video signal.
9. The method according to claim 1, wherein said band-pass
filtering of said video signal comprises: interpolating samples of
said images of said video signal to obtain interpolated samples;
and multiplying said interpolated samples with respective anti-blur
filter coefficients and summing the corresponding products to
obtain samples of images of said band-pass filtered video
signal.
10. The method according to claim 9, wherein said anti-blur filter
is a 1D anti-blur filter that is rotated according to a direction
of said estimated motion vectors, and wherein said samples of said
images of said video signals are interpolated to positions of taps
of said rotated anti-blur filter.
11. The method according to claim 9, wherein said anti-blur filter
coefficients are independent of said estimated motion vectors.
12. The method according to claim 9, wherein a spacing of said
anti-blur filter coefficients depends on the length of said
estimated motion vectors.
13. The method according to claim 10, wherein said samples of said
images of said video signal that are interpolated are located close
to lines that interconnect the filter taps of said rotated
anti-blur filter.
14. The method according to claim 10, wherein said samples of said
images of said video signal that are interpolated are located in a
region that perpendicularly extends to both sides from lines that
interconnect the filter taps of said rotated anti-blur filter.
15. The method according to claim 9, wherein said interpolation
comprises an at least partial averaging of said samples of said
images of said video signal.
16. The method according to claim 1, wherein said band-pass
filtering of said video signal further comprises: determining 2D
band-pass filters from a pre-defined set of 2D band-pass filters in
dependence on said estimated motion vectors; and filtering said
video signal with said determined 2D band-pass filters.
17. The method according to claim 16, wherein said determining of
said 2D band-pass filters comprises interpolating 2D band-pass
filters from 2D band-pass filters of said pre-defined set of 2D
band-pass filters.
18. The method according to claim 1, wherein said band-pass
filtered video signal is further subject to noise suppression
processing before being combined with said video signal.
19. A computer-readable medium embodying a computer program with
instructions operable to cause a processor to perform the method of
claim 1.
20. A computer-readable medium embodying a computer program with
instructions operable to cause a processor to perform the method of
claim 9.
21. A device for reducing motion blur of images of a video signal
shown on a hold-type display, comprising: means arranged for
estimating motion vectors of moving components in said images of
said video signal; means arranged for band-pass filtering said
video signal with respect to a spatial frequency domain, wherein
said band-pass filtering at least partially depends on said
estimated motion vectors, and wherein with increasing length of
said estimated motion vectors, the pass-band of said band-pass
filtering adaptively shifts from high spatial frequencies to medium
spatial frequencies, wherein band-pass filtering includes anti-blur
filtering performed with an anti-blur filter that comprises a
one-dimensional filter with a variable tap spacing that depends on
said length of said estimated motion vectors; and means for
combining said video signal and said band-pass filtered video
signal to produce an input video signal for said hold-type display.
Description
FIELD OF THE INVENTION
This invention relates to a method for reducing motion blur of
images of a video signal shown on a hold-type display.
BACKGROUND OF THE INVENTION
Over the last years, the traditional cathode ray tube (CRT) display
has had to face increasing competition from alternative display
principles, which are mainly based on active-matrix technology. In
particular, active-matrix liquid crystal displays (AM-LCDs) have
increased in performance and decreased in price so dramatically,
that the market share of the CRT is decreasing at a rapid pace. The
main differentiating feature of these new display principles is
their size: LCDs are thin, flat and lightweight. This has enabled
the first market for these displays: laptop computers. By now, the
LCD has also almost taken over the desktop monitor market, where
not only its size has made the difference, but also its uniform,
sharp, and flicker-free picture reproduction. Nowadays, the CRT is
also having to face competition from the LCD in its last
stronghold: television.
To make a good television display, the LCD has had to overcome
previous drawbacks, for example a limited viewing angle and color
performance. However, the CRT is still unbeaten in one major
aspect: motion portrayal. In that area, LCDs perform much worse,
since the LC-molecules that provide the basic display effect react
slowly to image changes. This causes an annoying smearing
(blurring) of moving objects, which makes the LCD unsuited for
video applications. Therefore, a lot of effort has been put into
speeding up the response of LC materials. This can be done by
applying better materials, or by improved LC cell design. There is
also a well known method for response time improvement based on
video processing, called `overdrive`. Overdrive improves the
response speed of the LC pixels by changing the drive values
depending on the applied gray level transition. This enables a
reduction of the response time to within the frame period.
Currently, the best displays available list response times below
the frame period (17 ms at 60 Hz). This is a crucial value, since
the worst blurring artifacts are prevented for an LCD that can
respond to image changes within the frame period.
However, speeding up the response of LC materials to lower values
is not enough to completely avoid motion blur. This is caused by
the active matrix principle itself, which exhibits a
sample-and-hold characteristic, causing light emission during the
whole frame time (hold-type display). This is a major difference
with the very short (microsecond) light flashes produced by the
phosphors of the CRT (impulse-type display). It is well known that
this prolonged light emission does not match very well with the way
humans perceive moving images. As will be further explained in the
next sections, the human eye will track moving objects on the
screen, thereby imaging the light, belonging to each fixed point in
a frame, onto a series of points on the retina. This `point
spreading` results in a loss of sharpness of moving objects.
The basic function of a display system is to reconstruct the
physical light emissions, corresponding to the original image, at
the correct position and time on the screen from the received
space-time discrete video signal. The characteristics of this
reconstruction process, especially when combined with
characteristics of the human visual system, can explain many image
quality artifacts that occur in practical display systems.
The very basic representation of the signal chain 1 from original
to displayed image is shown in FIG. 1. The original scene,
represented as a time varying image, is a space-time-continuous
intensity function I.sub.c({right arrow over (x)},t), where {right
arrow over (x)} has two dimensions: {right arrow over
(x)}=(x,y).sup.T. This original image is sampled by the camera 100
in time and space. Since the spatial sampling is outside the scope
of this specification, we will refer to it only occasionally from
now on. The temporal behavior, however, will be the main focus for
the remainder of this specification. The sampling process is
described by: I.sub.s({right arrow over (x)},t)=I.sub.c({right
arrow over (x)},t).LAMBDA.({right arrow over (x)},t), (1)
where .LAMBDA.({right arrow over (x)},t) is a three-dimensional
lattice of .delta.-impulses. We can assume a rectangular sampling
lattice, which is described by sampling intervals .DELTA.{right
arrow over (x)}=(.DELTA.x,.DELTA.y) and .DELTA.t:
.LAMBDA..function..fwdarw..times..delta..function..DELTA..times..times..d-
elta..function..DELTA..times..times..delta..function..DELTA..times..times.
##EQU00001##
The reconstruction of the physical light emission by the display
101 can be described by a convolution with the display aperture
(also known as reconstruction function or point spread function).
This aperture is also a function of space and time: A({right arrow
over (x)},t). The image, as produced by the display 101,
becomes:
.function..fwdarw..function..fwdarw..function..fwdarw..function..fwdarw..-
LAMBDA..function..fwdarw..function..fwdarw. ##EQU00002##
The two operations of sampling and reconstruction account for a
number of characteristic differences between the displayed image
and the original image. These are best described by a frequency
domain description, so we apply the Fourier transform F(F({right
arrow over (x)},t))=F.sup.f({right arrow over (f)}.sub.x,f.sub.t)
to Eq. (3): I.sub.d.sup.f({right arrow over
(f)}.sub.x,f.sub.t)=(I.sub.c.sup.f({right arrow over
(f)}.sub.x,f.sub.t)*.LAMBDA..sup.f({right arrow over
(f)}.sub.x,f.sub.t))A.sup.f({right arrow over (f)}.sub.x,f.sub.t),
(4)
where the Fourier transform .LAMBDA..sup.f({right arrow over
(f)}.sub.x,f.sub.t) of lattice .LAMBDA.({right arrow over (x)},t)
is the reciprocal lattice, with spacings (.DELTA.x).sup.-1,
(.DELTA.y).sup.-1 and (.DELTA.t).sup.-1 (the frame rate).
The spatio-temporal spectrum of the original image, the sampled
image, the displayed image and the finally perceived image as a
function of the normalized temporal frequency f.sub.t.DELTA.t and
the normalized spatial frequency f.sub.x.DELTA.x are depicted in
the four plots of FIG. 2, respectively, for the case of an
impulse-type (CRT) display. To simplify the illustration, we omit
the spatial repeats, as if the signal was continuous in the spatial
dimension. For the displayed images, this is equivalent to assuming
that the spatial dimension has been reconstructed perfectly, i.e.
the original continuous signal was spatially band-limited according
to the Nyquist criterion, and the reconstruction effectively
eliminates the repeat spectra.
In the temporal dimension, the impulse nature of the light emission
gives a flat reconstruction spectrum. As a consequence of this flat
spectrum, the temporal frequencies in the baseband
f.sub.t<(2.DELTA.t).sup.-1 are not attenuated, but also at least
the lowest order repeats are passed.
The image, as it is finally perceived by the viewer, is also
determined by the characteristics of the human visual system (HVS).
In the temporal domain, the HVS mainly behaves as a low-pass
filter, since it is insensitive to higher frequencies. The fourth
plot of FIG. 2 shows that the perceived image is identical to the
original image (cf. first plot of FIG. 2), if we assume that the
eye's low-pass eliminates all repeat spectra. This assumption is
not always true, which leads to one of the most widely known
artifacts in display systems: large area flicker. This is caused by
the first repeat spectrum (at low spatial frequencies) that is not
completely suppressed for frame rates approximately smaller than 75
Hz.
Active-matrix displays like LCDs do not have an impulse-type light
emission. The fastest displays that are currently available have
response times shorter than the frame period. However, even these
will still have a light emission during the whole frame period due
to the sample-and-hold behavior of the active matrix and the
continuous illumination by the backlight. This behavior results in
a temporal "box" reconstruction function with a width equal to the
hold time T.sub.h. In the frequency domain, this becomes a sinc
characteristic: A.sup.f({right arrow over (f)}.sub.x,f.sub.t)=sin
c(.pi.f.sub.tT.sub.h) (5)
The spectrum of the sampled image, of the aperture A({right arrow
over (x)},t), of the displayed image and of the finally perceived
image for such a hold-type display are depicted in the four plots
of FIG. 3, respectively. This immediately shows a distinctive
advantage of hold-type displays over impulse-type displays: the
sinc characteristic suppresses the repeat spectra in the displayed
image (cf. the third plot of FIG. 3), and even has zero
transmission at the sampling frequency. This eliminates large area
flicker at all frame rates.
It may seem that the sample-and-hold behavior of the hold-type
displays results in a better display than an impulse-type light
emission. For static images this is indeed the case. However, the
conclusion changes for a moving image: I.sub.m({right arrow over
(x)},t)=I.sub.c({right arrow over (x)}+{right arrow over (v)}t,t),
(6)
where {right arrow over (v)} is the speed of the moving image over
the screen, measured here in the same units that are used for
{right arrow over (x)} and t. When the sampling intervals
.DELTA.{right arrow over (x)}=(.DELTA.x,.DELTA.y) are known, {right
arrow over (v)} can also be expressed in "pixels per frame". This
corresponds to the "motion vector" or "frame displacement
vector".
Eq. (6) can also be transformed to the frequency domain, where it
becomes: I.sub.m.sup.f({right arrow over
(f)}.sub.x,f.sub.t)=I.sub.c.sup.f({right arrow over
(f)}.sub.x,f.sub.t-{right arrow over (v)}{right arrow over
(f)}.sub.x). (7)
This movement results in a shearing of the spectrum as shown in the
second plot of FIG. 4, in comparison to the spectrum of the still
original image in the first plot of FIG. 4. The shearing of the
spectrum reflects that spatial variations in a moving object will
generate temporal variations.
This moving image is then sampled (cf. the third plot of FIG. 4)
and reconstructed in the display chain, after which it reaches the
eye. The perception of moving images is characterized by another
important property of the HVS: the eye tracking. The viewer tries
to follow moving objects across the screen in order to produce a
static image on the retina. This mechanism is well studied, and
enables the HVS to perceive moving images with a high level of
detail. The image on the retina of an eye tracking viewer is
described by the inverse of the relations in Eqs. (6) and (7):
I.sub.e({right arrow over (x)},t)=I.sub.d({right arrow over
(x)}-{right arrow over (v)}t,t) I.sub.e.sup.f({right arrow over
(f)}.sub.x,f.sub.t)=I.sub.d.sup.f({right arrow over
(f)}.sub.x,f.sub.t+{right arrow over (v)}{right arrow over
(f)}.sub.x) (8)
The whole chain 5 from original image to perceived image,
comprising a motion instance 500 (due to moving objects), a
sampling instance 501 (e.g. a camera), a reconstruction instance
502 (e.g. a display), a tracking instance 503 (the viewer tracking
the motion) and a low-pass filter 504 (the eye), is shown in FIG.
5. Substituting Eq. (3) in Eq. (8) and applying Eq. (7), gives the
image as projected onto the retina of the eye tracking viewer:
.function..fwdarw..function..fwdarw..fwdarw..fwdarw..LAMBDA..function..fw-
darw..fwdarw..fwdarw..function..fwdarw..fwdarw..fwdarw..function..fwdarw..-
LAMBDA..function..fwdarw..fwdarw..fwdarw..function..fwdarw..fwdarw..fwdarw-
. ##EQU00003##
The perceived image I.sub.p.sup.f({right arrow over
(f)}.sub.x,f.sub.t) after low-pass filtering by the eye is shown in
the third plot of FIG. 6 for an impulse-type display, and in the
fourth plot of FIG. 7 for a hold-type display, wherein the plots of
FIGS. 6 and 7 complement the plots of FIG. 4, respectively. The
image after the eye low-pass is obtained by only looking at the
frequencies f.sub.t.apprxeq.0, again assuming perfect
reconstruction in the spatial domain. There we can see that the
effect of the temporal aperture function of the display, combined
with eye tracking, can be described as spatial filtering of moving
images:
.function..fwdarw..function..fwdarw..function..fwdarw..fwdarw..fwdarw..fu-
nction..fwdarw..function..fwdarw. ##EQU00004##
with the spatial low-pass filter H.sup.f({right arrow over
(f)}.sub.x)=sin c(.pi.{right arrow over (v)}{right arrow over
(f)}.sub.xT.sub.h). (11)
The filter H.sup.f({right arrow over (f)}.sub.x) of Eq. (11)
depends on the speed of motion {right arrow over (v)} and the hold
time (frame period) T.sub.h.
FIG. 8 schematically depicts the amplitude response of this filter
as a function of motion (speed) |{right arrow over (v)}| (in pixels
per frame) and normalized spatial frequency f.sub.x.DELTA.x along
the motion direction
.fwdarw..fwdarw..fwdarw. ##EQU00005## wherein the white region
represents amplitudes between 1 and 0.5 (low attenuation) and
wherein the shaded region represents amplitudes between 0.4 and 0
(high attenuation).
Although the temporal "hold" aperture is beneficial with respect to
large area flicker, it will cause a spatial blurring of moving
objects on the retina of the viewer. Higher spatial frequencies
will be attenuated by the sinc characteristic, and the spatial
frequency from which the attenuation starts will get smaller with
increase with speed, thus affecting an extended spatial frequency
region. Furthermore, this blurring will only occur along the motion
direction. The sharpness perpendicular to the motion of each object
is not affected.
Eq. (11) suggests that, in order to decrease this effect, the hold
time T.sub.h must be decreased. This can be achieved in two ways.
First of all, the frame rate can be increased. In order to have the
required effect, this must be done with a motion-compensated frame
rate conversion, since a simple frame repetition will result in the
same effective hold time. Secondly, without changing the frame
rate, we can decrease the period (or better: duty-cycle) of light
emission. For LCDs, this can be realized by switching the backlight
on only during a part of the frame time, using a so-called
"scanning backlight".
A third option for decreasing motion blur due to the
sample-and-hold effect, based on Eq. (11), is to use only video
processing, and does not require modification of display or
backlight. The low pass filtering of the display+eye combination
903 (consisting of reconstruction 901 by the display and
tracking/low-pass filtering 902 by the viewer/eye) is
pre-compensated in the video domain, as shown in the display chain
9 of FIG. 9. This can be achieved by using the inverse filter 900
of the filter H.sup.f({right arrow over (f)}.sub.x) of Eq.
(11):
.function..fwdarw..function..pi..times..fwdarw..times..times..fwdarw..tim-
es. ##EQU00006##
The inverse filter H.sub.inv.sup.f({right arrow over (f)}.sub.x) is
a purely spatial filter, reflecting the observation that the
temporal aperture of the display, combined with eye tracking,
results in a spatial low-pass filter H.sup.f({right arrow over
(f)}.sub.x). The cascade 9 of the inverse filter 900 and the
display+eye combination 903 further along the chain should result
in a perceived image that approaches the original image as well as
possible.
EP 0 657 860 A2 discloses the use of an approximation {tilde over
(H)}.sub.inv.sup.f({right arrow over (f)}.sub.x) of such a
pre-compensation filter H.sub.inv.sup.f({right arrow over
(f)}.sub.x) 900 in the shape of a speed-dependent high spatial
frequency enhancement filter (or high spatial frequency boosting
filter), which enhances the spectrum of the video signal at high
spatial frequencies according to the speed of the moving
components, wherein said spectrum at high spatial frequencies is
related to moving components in the images of the video signal.
Therein, the cut-off frequency of the spatial frequency enhancement
filter (from which on the enhancement starts) is adjusted according
to motion vectors that are estimated by a motion vector estimator.
The spatial frequency enhancement filter {tilde over
(H)}.sub.inv.sup.f({right arrow over (f)}.sub.x) deployed in EP 0
657 860 A2 is not the exact inverse filter H.sub.inv.sup.f({right
arrow over (f)}.sub.x) as defined in Eq. (12), because the
restoration of those frequencies which have been attenuated to very
low levels (for instance in the zeroes of the spatial low pass
filter H.sup.f({right arrow over (f)}.sub.x) of Eq. (11)), e.g.
below noise thresholds, can not realistically be achieved.
FIG. 10 depicts the transfer function of the spatial low-pass
filter H.sup.f({right arrow over (f)}.sub.x) 1000 of Eq. (11), of
the inverse filter H.sub.inv.sup.f({right arrow over (f)}.sub.x)
1001 of Eq. (12), and of an approximation 1002 of the inverse
filter H.sub.inv.sup.f({right arrow over (f)}.sub.x) of Eq. (12) as
a function of the spatial frequency, wherein said approximation
1002 is similar to the high spatial frequency enhancement filter of
EP 0 657 860 A2.
Spatial frequency enhancement filters as disclosed in EP 0 657 860
A2 also enhance the high spatial frequency components of noise that
is present in the sampled images of the video signal. However, in
flat (undetailed) image parts, the motion estimator has a high
probability of estimating the wrong motion vector that determines
the cut-off frequency of the spatial frequency enhancement filter,
resulting in undesirable noise amplification at high spatial
frequency enhancement filter gains, which significantly degrade the
quality of the images of the video signal.
SUMMARY OF THE INVENTION
In view of the above-mentioned problem, it is, inter alia, an
object of the present invention to provide improved methods,
computer programs, computer program products and devices for
reducing motion blur of images of a video signal shown on a
hold-type display.
A method is proposed for reducing motion blur of images of a video
signal shown on a hold-type display, comprising estimating motion
vectors of moving components in said images of said video signal;
band-pass filtering said video signal with respect to a spatial
frequency domain, wherein said band-pass filtering at least
partially depends on said estimated motion vectors, and wherein
with increasing length of said estimated motion vectors, the
pass-band of said band-pass filtering adaptively shifts from high
spatial frequencies to medium spatial frequencies; and combining
said video signal and said band-pass filtered video signal to
produce an input video signal for said hold-type display.
Said hold-type display may be understood as a non-stroboscopic
display, i.e. images are shown on the display during a time period
that is not negligible with respect to the image period of the
images. Examples for hold-type or non-stroboscopic display are for
instance non-emissive displays, such as Liquid Crystal Displays
(LCD), Plasma Panel Displays (PDP) and Thin Film Transistor (TFT)
displays, which may for instance consist of a display panel having
a row and column array of picture elements (pixels) for modulating
light, means for illuminating the display panel from the from or
back side, and drive means for driving the pixels in accordance
with an applied input video signal. Further examples of hold-type
displays are emissive displays, such as Organic Light Emitting
Diode (O-LED) displays or Polymer Light Emitting Diodes (Poly-LED)
displays, which may for instance consist of a display panel having
a row and column array of pixels (LEDs) and drive means for driving
the pixels (LEDs) in accordance with an applied input video signal.
Therein, the pixels (LEDs) emit and modulate light by themselves
without requiring illumination from the front or back side.
On said hold-type displays, images of a video signal are displayed,
wherein said video signal is composed of a sequence of images and
wherein said images are represented by image samples, for instance
picture elements (pixels). The images of said video signal that
contains components or objects moving from one image to the next
suffer from motion blur when being viewed by a viewer, wherein said
motion blur may be described by a spatial frequency domain low-pass
filtering of said images.
Motion vectors of said moving components in said images of said
video signal are estimated, for instance by means of a
block-matching algorithm, that determines the displacement of
components from one image to the next. Motion vector then may be
associated with said moving components or with the samples or
pixels of said moving components.
Said video signal is band-pass filtered in the spatial frequency
domain, and subsequently said band-pass filtered video signal and
said video signal are combined, for instance added, to produce an
input video signal for said hold-type display. Different band-pass
filtering may be applied to different components or pixels of said
images of said video signal.
Said band-pass filtering is represented by a band-pass filter that
has a transfer function in the spatial frequency domain with a
pass-band section where the transfer function is non-zero and
stop-band sections at the left and the right of said pass-band
where the transfer function is substantially zero.
Said band-pass filtering at least partially depends on said
estimated motion vectors, for instance, said band-pass filtering
may only be performed in the direction of said estimated motion
vectors. With increasing length of the estimated motion vectors
(i.e. increasing speed of moving components in said images of said
video signal), the pass-band of said band-pass filter moves from
higher spatial frequencies towards medium spatial frequencies,
wherein this movement is adaptive with respect to the length of the
estimated motion vectors.
The combination of the band-pass filtered video signal and the
original video signal can be considered as a speed-dependent
medium-frequency enhancement (or boosting) filter structure, which
limits the enhancement of components of the video signal to a
medium spatial frequency range and which adaptively moves this
frequency range from higher spatial frequencies towards lower
spatial frequencies when the amount of motion in the images of the
video signal increases.
The present invention sets out from a first observation that, for
high speeds, the spatial frequency low-pass filter that causes the
blurring has a considerable attenuation at already very low spatial
frequencies. A second observation is that the human visual system
is more sensitive to the lower spatial frequencies, and that the
higher frequencies generally have a lower signal-to-noise ratio.
Finally, according to a third observation, it is noticed by the
present invention that in common video material, moving objects
will not contain the highest frequencies due to the limitations of
the camera (camera blur). For this reason, viewers are used to
losing some detail at high speed, although not to the extent (up to
lower spatial frequencies) that is caused by LCD panels.
In contrast to prior art techniques, wherein always high frequency
boosting is performed and wherein only the spatial frequency where
boosting starts is lowered with increasing motion, according to the
present invention, priority is thus given to the compensation of
the lowest frequencies that are affected by blurring, i.e. the
medium spatial frequencies, and the highest spatial frequencies are
basically left unchanged. This leads to a considerate improvement
of motion blur reduction in video signals as compared to the prior
art techniques.
According to a preferred embodiment of the present invention, said
band-pass filtering comprises low-pass filtering and anti-blur
filtering in cascaded form. Said anti-blur filtering may for
instance be represented by a high-pass filter that is at least
partially adapted to the display characteristics of said display,
and said low-pass filtering and subsequent-high-pass filtering then
may result in a band-pass filtering. Said shift of said pass-band
of said band-pass filtering may for instance be accomplished by
shifting the lower edge of the pass-band of the high-pass filter
towards lower frequencies with increasing speed.
According to a preferred embodiment of the present invention, said
anti-blur filtering is performed with an anti-blur filter that
approximates an inverted low-pass filter. Said low-pass filter may
for instance cause blurring and may depend on the length of the
motion vectors (i.e. the speed of moving components in said
images), so that to compensate for the blurring, the inverse of
said low-pass filter has to be applied to said video signal, and
wherein said inverse then also depends on the length of said motion
vectors.
According to a preferred embodiment of the present invention, said
anti-blur filtering is performed with an anti-blur filter, and
wherein said anti-blur filter is a one-dimensional filter with
fixed filter coefficients and a variable tap spacing that depends
on said length of said estimated motion vectors. Said anti-blur
filter may for instance be applied along the direction of said
estimated motion vectors. By varying said tap spacing, the spatial
frequency transfer function of said anti-blur filter may be
changed, for instance, with increased tap spacing, a pass-band of
said anti-blur filter may shift towards lower frequencies.
According to a preferred embodiment of the present invention, said
anti-blur filtering is performed in the direction of said estimated
motion vectors. This is particularly advantageous if motion blur
only occurs in the direction of the motion vectors, so that, when
also filtering only towards the direction of the motion vectors to
reduce motion blur, only a minimum of noise enhancement occurs.
According to a preferred embodiment of the present invention, said
low-pass filtering is performed in the direction of said estimated
motion vectors. To reduce the number of pixels involved in the
filtering process, and thus to reduce the computational complexity,
it may be advantageous to perform the low-pass filtering only in
the direction of the estimated motion vectors.
According to a preferred embodiment of the present invention, said
low-pass filtering is performed both in a direction perpendicular
and in a direction parallel to the direction of said estimated
motion vectors. Performing the low-pass filtering also in a
direction perpendicular to the direction of the estimated motion
vectors may contribute to average out noise contained in said
samples of said images of said video signal.
According to a preferred embodiment of the present invention, said
low-pass filtering is at least partially implemented by an
interpolation of samples of said images of said video signal. Said
interpolation may for instance contain averaging over several
pixels, wherein said averaging can be considered as low-pass
filtering.
According to a preferred embodiment of the present invention, said
band-pass filtering of said video signal comprises interpolating
samples of said images of said video signal to obtain interpolated
samples, multiplying said interpolated samples with respective
anti-blur filter coefficients, and summing the products to obtain
samples of images of said band-pass filtered video signal. Said
interpolation may for instance be a 2D interpolation of samples to
special positions, for instance to the positions of the taps of a
1D or 2D anti-blur filter. Said interpolation may for instance be
based on polynomial, rational, or trigonometrical interpolation, or
on any other interpolation technique.
According to a preferred embodiment of the present invention, said
anti-blur filter is a 1D anti-blur filter that is rotated according
to the direction of said estimated motion vectors, and wherein said
samples of said images of said video signals are interpolated to
the positions of the taps of said rotated 1D anti-blur filter.
According to a preferred embodiment of the present invention, said
anti-blur filter coefficients are independent of said estimated
motion vectors. Said filter coefficients may for instance be
pre-defined filter coefficients that are optimized with respect to
the display characteristics of said hold-type display.
According to a preferred embodiment of the present invention, the
spacing of said anti-blur filter coefficients depends on the length
of said estimated motion vectors. Said spacing, i.e. the spatial
distance between the taps of said anti-blur filter, may increase
with increasing length of said estimated motion vectors.
According to a preferred embodiment of the present invention, said
samples of said images of said video signal that are interpolated
are located close to lines that interconnect the filter taps of
said rotated anti-blur filter.
According to a preferred embodiment of the present invention, said
samples of said images of said video signal that are interpolated
are located in a region that perpendicularly extends to both sides
from said lines that interconnect the filter taps of said rotated
anti-blur filter. Said interpolation then contains an additional
averaging of samples perpendicular to the direction in which
anti-blur filtering is applied, and thus perpendicular to the
direction of the estimated motion vectors. This may contribute to
average out noise that is contained in said samples.
According to a preferred embodiment of the present invention, said
interpolation comprises an at least partial averaging of said
samples of said images of said video signal. Said averaging may
contribute to average out noise and/or to perform an additional
low-pass filtering of said video signal.
According to a preferred embodiment of the present invention, said
band-pass filtering of said video signal comprises determining 2D
band-pass filters from a pre-defined set of 2D band-pass filters in
dependence on said estimated motion vectors and filtering said
video signal with said selected 2D band-pass filters. Said
pre-defined set of 2D band-pass filters may for instance comprise
pre-computed 2D band-pass filters for a plurality of possible
lengths and directions of motion vectors in a tabular structure, so
that a 2D band-pass filter may be chosen from said pre-defined set
by selecting the pre-computed 2D band-pass filter that is
associated with the length and direction of a motion vector that is
closest to the length and direction of said estimated motion
vector.
According to a preferred embodiment of the present invention, said
determining of said 2D band-pass filters comprises interpolating 2D
band-pass filters from 2D band-pass filters of said pre-defined set
of 2D band-pass filters. Said 2D band-pass filter may also be
determined from said pre-defined set of 2D band-pass filters by
interpolating two or more of the 2D band-pass filters contained in
said set, depending on the relation between the length and
direction of the estimated motion vector and the length and
direction of the motion vectors for which the 2D band-pass filters
in said pre-defined set of band-pass filters were computed. Said
interpolating may contribute to reducing the required size of said
pre-defined set of 2D band-pass filters.
According to a preferred embodiment of the present invention, said
band-pass filtered video signal is further subject to noise
suppression processing before being combined with said video
signal. Said noise suppression processing may for instance suppress
noise by discarding the low-amplitude high spatial frequencies by
coring, and/or by filtering said band-pass filtered signal with a
non-linear order-statistical filter. Then frequency enhancement is
only performed in regions where there is sufficient signal, as
these are also the regions where motion blur is most
objectionable.
A computer program is further proposed with instructions operable
to cause a processor to perform the above-mentioned method steps.
Said computer program may for instance be processed by a Central
Processing Unit (CPU) or any other processor integrated in a device
that is related to the displaying of said images of said video
signal, for instance a display, a television, or a monitor.
A computer program product is further proposed comprising a
computer program with instructions operable to cause a processor to
perform any of the above-mentioned method steps. Said computer
program product may for instance be a removable storage medium such
as a disc, a memory stick, a memory card, a CD-ROM, DVD or any
other storage medium.
A device for reducing motion blur of images of a video signal shown
on a hold-type display is further proposed, said device comprising
means arranged for estimating motion vectors of moving components
in said images of said video signal, means arranged for band-pass
filtering said video signal with respect to a spatial frequency
domain, wherein said band-pass filtering at least partially depends
on said estimated motion vectors, and wherein with increasing
length of said estimated motion vectors, the pass-band of said
band-pass filtering adaptively shifts from high spatial frequencies
to medium spatial frequencies, and means for combining said video
signal and said band-pass filtered video signal to produce an input
video signal for said hold-type display.
Said device may for instance be realized as a separate unit
processing the video signals prior to sending them to a display.
Said device may also be integrated into a display, or into a device
that houses a display, as for instance a television, a monitor, a
system operating a head-mounted display, or a mobile multimedia
device such as a mobile phone or a PDA.
These and other aspects of the invention will be apparent from and
elucidated with reference to the embodiments described
hereinafter.
BRIEF DESCRIPTION OF THE FIGURES
In the Figures show:
FIG. 1: A schematic illustration of the display chain comprising a
still original image, a sampled image and a displayed image
according to the prior art;
FIG. 2: spatio-temporal frequency spectra (as a function of
normalized temporal frequency f.sub.t.DELTA.t and normalized
spatial frequency f.sub.x.DELTA.x) of original image I.sub.c,
sampled image I.sub.s, displayed image I.sub.d and perceived image
(after eye low-pass) I.sub.p corresponding to the sampling and
displaying of an image on an impulse-type display according to the
prior art;
FIG. 3: spatio-temporal frequency spectra of sampled image I.sub.s,
aperture function A (with color code white representing low
amplitudes and color code black representing high amplitudes),
displayed image I.sub.d and perceived image (after eye low-pass)
I.sub.p corresponding to the sampling and displaying of an image on
a hold-type display according to the prior art;
FIG. 4: spatio-temporal frequency spectra of still original image
I.sub.c, moving original image I.sub.m and sampled original image
I.sub.s according to the prior art;
FIG. 5: a schematic illustration of the display chain from a moving
original image to a finally perceived image according to the prior
art;
FIG. 6: spatio-temporal frequency spectra of displayed image
I.sub.d, image after eye tracking I.sub.e and image after eye
low-pass I.sub.p for an impulse-type display according to the prior
art, complementing FIG. 4;
FIG. 7: spatio-temporal frequency spectra of aperture function A,
displayed image I.sub.d, image after eye tracking I.sub.e and image
after eye low-pass I.sub.p for a hold-type display according to the
prior art, complementing FIG. 4;
FIG. 8: schematic amplitude response of the spatial filter
H.sup.f({right arrow over (f)}.sub.x) due to temporal display
aperture and eye tracking as a function of spatial frequency and
speed, with speed measured in pixels per frame, f.sub.x expressed
in cycles per pixels, and T.sub.h=1 frame);
FIG. 9: a schematic illustration of the display chain from video
signal to perceived image with a pre-compensation filter according
to the prior art;
FIG. 10: the transfer function of the display+eye filter
H.sup.f({right arrow over (f)}.sub.x), the corresponding inverse
filter H.sub.inv.sup.f({right arrow over (f)}.sub.x), and an
approximation thereof as a function of the spatial frequency
according to the prior art, for a speed of three pixels per
frame;
FIG. 11: an exemplary speed-dependent medium frequency boosting
filter structure for reducing motion blur according to the present
invention;
FIG. 12: a schematic illustration of the rotation and the
speed-dependent tap spacing of the 1D anti-blur filter contained in
the filter structure of FIG. 11 according to the present
invention;
FIG. 13: exemplary transfer functions of the filter structure
according to FIG. 11 (solid lines) and of the ideal inverse filter
(dashed lines) as a function of the normalized spatial frequency
for different speeds according to the present invention;
FIG. 14: a schematic illustration of the samples of the video
sampling grid involved in the interpolation of samples to the 1D
anti-blur filter tap positions according to the present
invention;
FIG. 15a: a schematic illustration of the samples of the video
sampling grid involved in the interpolation of samples to the 1D
anti-blur filter tap positions with increased averaging according
to the present invention;
FIG. 15b: a schematic illustration of the samples of the video
sampling grid involved in the interpolation of samples to the 1D
anti-blur filter tap positions with increased number of filter taps
according to the present invention;
FIG. 16: exemplary transfer functions of a medium frequency
boosting filter structure according to FIG. 11 (solid lines) and of
the ideal inverse filter (dashed lines) as a function of the
normalized spatial frequency for different speeds according to the
present invention;
FIG. 17: a schematic amplitude response of the combination of the
filter structure according to FIG. 16 and the display+eye
combination as a function of motion (in pixels per frame) and
normalized spatial frequency; and
FIG. 18: a schematic illustration of the samples of the video
sampling grid involved in the interpolation of samples to the 1D
anti-blur filter tap positions under additional usage of samples
positioned farther apart from the line defined by the filter taps
according to the present invention;
DETAILED DESCRIPTION OF THE INVENTION
The present invention sets out from the observation that the
display+eye filter H.sup.f({right arrow over (f)}.sub.x) of Eq.
(11), as illustrated in FIG. 8, at high speeds has a considerable
attenuation at already very low spatial frequencies. Furthermore,
it is recognized that the human visual system is more sensitive to
the lower spatial frequencies, and that the higher frequencies
generally have a lower signal-to-noise ratio. Furthermore, the
present invention recognized that in common video material, moving
objects will not contain the highest frequencies due to the
limitations of the camera (camera blur). For this reason, viewers
are used to losing some detail at high speed, although not to the
extent (up to lower spatial frequencies) that is caused by LCD
panels.
According to the present invention, in case of high speeds, it is
thus proposed to give priority to the compensation of the lowest
affected frequencies, and to leave the highest frequencies
basically unchanged. This transforms the prior art high-frequency
boosting filter, which serves as an approximation of the inverse
filter of Eq. (12), cf. FIG. 9, into a medium-frequency boosting
filter, which limits the amplification of the higher frequencies at
high speeds, and only compensates the lowest frequencies.
FIG. 11 shows a corresponding embodiment of a filter structure 11
of the present invention. Pixels of images of a video signal are
fed into a motion estimator instance 1102, in which both the length
and the direction of motion vectors associated with moving objects
in said images of said video signal are estimated, for instance via
a 3D recursive block matching algorithm or similar techniques. Said
pixels of images of a video signal are also fed into a 2D
interpolation instance 1100. This interpolation instance 1100 uses
a 2D neighborhood around a current pixel taken from an image of
said video signal, and, based on the estimated direction of the
motion vector that is associated with said current pixel, returns a
1D series (line) of samples to the 1D anti-blur filter 1101. The
coefficients of said 1D anti-blur filter may be fixed, they may for
instance be pre-determined and adapted to the characteristics of
the display.
The samples resulting from the interpolation correspond to the taps
of the 1D anti-blur filter 1101. These samples are subsequently
multiplied with the 1D anti-blur filter tap coefficients and
accumulated, to result in a single "correction" value for the
current pixel. This operation is not a conventional convolution
filtering, since the applied line of samples can totally change
from one pixel to the next, if the motion vector changes. Said 2D
interpolation and said subsequent multiplication of the
interpolated pixels with the filter tap coefficients can be
considered as an orientation of the 1D anti-blur filter kernel
along the motion vectors by rotating the 1D filter kernel, which
makes the filtering actually a 2D filtering. The interpolation
accounts for the fact that the rotated 1D anti-blur filter taps
generally do not coincide with sample (pixel) positions in the
image. This interpolation may for instance be a bi-linear
interpolation or any other type of interpolation.
The positions of these interpolated pixels (or the corresponding 1D
anti-blur filter taps) vary not only with the direction of the
motion vector, but also lie at a larger distance from the central
tap for higher speeds. This shifts the response of the 1D anti-blur
filter towards lower frequencies for increasing length of the
motion vectors. This is symbolically illustrated in FIG. 11 by
inputting the length of the motion vectors (or speed of components
in the images of the video signal) as estimated by the motion
estimation instance 1102 into the 1D anti-blur filter 1101. It is
readily seen that, in particular when the filter tap coefficients
of the 1D anti-blur filter 1101 are fixed, the spacing of the 1D
anti-blur filter taps can also be adjusted during the 2D
interpolation in interpolation instance 1102. Then the estimated
length and direction of the motion vectors is fed from said motion
estimation instance 1102 to said 2D interpolation instance
1100.
The filtered pixels as output by the 1D anti-blur filter 1101 may
then be fed into an optional noise reduction instance 1103. This
noise reduction instance may for instance perform "coring" on said
pixels, i.e. noise is suppressed by discarding the low-amplitude
high frequencies, and/or filter said pixels with a non-linear
order-statistical filter. These techniques will contribute to
applying the frequency enhancement only in regions where there is
sufficient signal, as these are also the regions where motion blur
is most objectionable.
The filtered and possibly noise-reduced pixels are then added to
the pixels of the original video signal by means of an adder 1104,
and then are fed to a hold-type display.
From the structure of the filter 11, it is readily seen that the
display is fed with the sum of the original video signal and a
filtered version of said original video signal, wherein said
filtering is specific for pixels or groups of pixels within the
images of said video signal and only takes place along the
estimated motion vectors. Furthermore, as will be explained in the
following, said 2D interpolation and 1D filtering implement a
band-pass filtering that only takes place in a band-limited
frequency range that depends on the estimated length of the motion
vectors, wherein said frequency range is shifted from high
frequencies to medium frequencies with increasing motion in said
video signal. Optionally the enhancement of the frequency
components within the band-limited frequency range can be
suppressed by said noise reduction instance 1103. The complete
system 11 thus represents a medium frequency boosting filter,
wherein the boosted frequency range moves from higher to lower
frequencies for increasing motion in the video signal.
FIG. 12 shows a portion of the video sampling grid 12 as dark
boxes, and different rotations and tap spacings of an exemplary
three-tap 1D anti-blur filter as gray boxes, wherein the three taps
are interconnected with dashed lines that indicate the direction of
the filtering. It is readily seen from FIG. 12 that the pixel
positions of the video sampling grid do not necessarily coincide
with the positions of the 1D anti-blur filter that is rotated
according to the direction of the estimated motion vectors. It can
also clearly be seen that the position of the center tap of the
three-tap 1D anti-blur filter remains constant when the tap spacing
increases due to increased length of the motion vectors (or, speed
of components in the images).
FIG. 13 shows the transfer function of the filter structure 11
(composed of 2D-interpolation, rotated 1D anti-blur filter and
adder) as a function of the normalized spatial frequency in solid
lines (1201a . . . 1204a), and also the transfer function of the
ideal inverse filter in dashed lines (1201b . . . 1204b), wherein
both the transfer function of the filter structure 11 and the ideal
inverse filter are given for different speeds, which decreases from
filters 1201a to 1204a and 1201b to 1204b, respectively. It is
readily seen from the ideal inverse filters, that with increasing
speed, the spatial frequency where the enhancement of the ideal
inverse filter starts is moving towards smaller spatial
frequencies. For fixed speeds, the transfer functions of the filter
structure 11 represent a good approximation of the corresponding
ideal inverse filter for small spatial frequencies. However, when
the taps of the 1D anti-blur filter of the filter structure 11 are
simply shifted away from the central tap at increasing speed, as
shown in FIG. 12, the transfer function becomes periodic, and high
frequencies can still pass the filter. This happens when input
samples are `skipped` during the filtering.
FIG. 14 shows which samples (the black boxes) on the video sampling
grid 14 (the white boxes) are used to calculate each interpolated
sample (for a bi-linear interpolation). The skipping of samples
between the filter taps, in particular between the center filter
tap and the respective left and right interpolated outer filter tap
is obvious in this example.
To solve this problem, the present invention proposes to change the
response of the filter structure 11, to actually suppress the very
highest frequencies for high speeds. This is achieved by using an
interpolation method that suppresses these frequencies before the
tap multiplications, i.e. that uses (averages) more original
samples to compute an interpolated sample.
FIG. 15a illustrates this principle. In contrast to FIG. 14, now
more than four samples are used for the interpolation of the
samples associated with the leftmost and rightmost filter tap.
An alternative approach to suppress the periodicity of the 1D
anti-blur filter for higher speeds is to first interpolate more
samples, and then to use a filter with more taps that suppresses
the high frequencies. This approach is depicted in FIG. 15b, where
the number of taps has been increased from 3 to 5.
The suppression of high frequencies at high speeds can also be
achieved by cascading the 1D anti-blur filter with a
speed-dependent low-pass filter, or by storing a number of (1D)
filters for various speeds. The resulting transfer functions 1601a
. . . 1604a of the filter structure for different speeds as a
function of the normalized spatial frequency, and the corresponding
ideal inverse filters 1601b . . . 1604b are shown in FIG. 16,
wherein speed decreases from filter 1601 to 1604, respectively.
From FIG. 16, it can be readily seen that the filter structure 11
of FIG. 11 now can be considered to consist of an all-pass filter
(the direct feed of the original video signal to the adder 1104)
and a band-pass filter (the combination of 2D interpolation and 1D
anti-blur filter) that are added to obtain the transfer functions
of FIG. 16. By subtracting "1" from the transfer functions 1601a .
. . 1604a of the filter structure, thus the transfer function of
the combination of 2D interpolation and 1D anti-blur filter is
obtained, which exhibits a band-pass characteristic. The pass-band
of this band-pass characteristic shifts from high spatial
frequencies to medium spatial frequencies with increasing speed,
wherein this shift is performed adaptively in response to the
estimated length of the motion vectors, which affects the tap
spacing of the 1D anti-blur filter. The rotation of the 1D
anti-blur filter response as performed by the 2D interpolation
ensures that the band-pass filtering is only applied along the
direction of the motion vectors.
FIG. 17 schematically depicts the amplitude of the combination of
the filter structure 11 and the display+eye combination as a
function of motion (in pixels per frame) and normalized spatial
frequency. Therein, the white area represents amplitudes between 1
and 0.5, and the shaded region represent amplitudes between 0.5 and
0). From the white area in FIG. 17b, it can clearly be seen that
with increasing speed, the enhancement of spectrum components at
large spatial frequencies, which is performed by the filter
structure according to FIG. 11, is significantly reduced in favor
of the spectrum components at medium and small frequencies.
To further reduce the impact of noise on the filtered video signal,
also a low-pass filtering perpendicular to the motion direction can
be beneficial, which can be achieved by also using samples further
away from the line of the motion in the 2D interpolation.
This concept is illustrated in FIG. 18, where the white boxes
denote the video sampling grid 18, the gray boxes denote the taps
of the rotated 1D five-tap filter and the black boxes denote the
samples used for the interpolation of samples towards the filter
tap positions. In contrast to FIG. 15b, it is noted that the region
that perpendicularly extends from the line defined by the filter
taps and that contains the samples that are used for the
interpolation is wider than in FIG. 15b, thus taking into account
more samples in perpendicular direction to the direction of the
motion vectors to increase the averaging effect and thus to
suppress noise.
The resulting filter thus has a low-pass behavior perpendicular to
the motion, and a band-pass behavior along the motion.
Finally, alternative to implementing the filters as a directional
dependent interpolation followed by a (1D) filtering, the filters
can be calculated for a number of angles and speeds (a number of
motion vectors), and stored in a table. The filtering then comes
down to applying a different 2D filter for each pixel, where the
coefficients of this filter are according to the principles
mentioned in this part of the specification. The number of stored
filters can be limited, when `intermediate` filters are calculated
(interpolated) based on the stored ones.
To evaluate the performance of the present invention, the filter
structure 11 according to FIGS. 11 and 17 was tested on an LCD-TV
simulation setup, which consists of a PC-based video streamer that
can play back stored sequences in real time, a DVI to LVDS panel
interface board, and a 30 inch LCD-TV panel (1280.times.768@60 Hz,
without additional processing). Although the panel had a listed
response time of 12 ms, a measurement was performed of the response
times for each gray level transition, and an average response time
of 20 ms was found. To further increase the response speed, (a
moderate amount of) overdrive was used to get the response time to
within one frame time.
By means of comparison with a CRT display, it could be observed
that there was not visibly more motion blur on the LCD than on the
CRT. Only for very critical (graphics-like) sequences, motion blur
was still visible.
The invention has been described above by means of preferred
embodiments. It should be noted that there are alternative ways and
variations which are obvious to a skilled person in the art and can
be implemented without deviating from the scope and spirit of the
appended claims.
* * * * *